NLBSE 2027

About NLBSE

Natural language artifacts, from requirements and design documents to issue reports, code comments, and user feedback, are created and (re)used throughout software development and evolution. Natural language processing (NLP) tools and techniques are commonly used to optimize many aspects of the software development life cycle relying on such artifacts. More recently, coding assistants and agentic workflows based on large language and large code models have introduced new opportunities and challenges, with natural language specifications increasingly serving as executable artifacts. Yet open questions remain about the suitability of specialized versus generalized NLP solutions, their computational efficiency and deployability on consumer hardware, and the scientific rigor and reproducibility of reported results.

The main objective of the two-day Natural Language-Based Software Engineering Workshop (NLBSE) is to bring together researchers and industrial practitioners from the NLP and software engineering communities to share experiences across this diversity of methods, artifacts, and application contexts. To keep discussions grounded in practice, the workshop also hosts tool competitions in which participants develop and evaluate NLP-based solutions on shared tasks and datasets.

Attending NLBSE

NLBSE 2027 is co-located with ICSE 2027. To attend NLBSE, you have to register for our workshop; registration will open on the official ICSE 2027 site.

Call for Papers

Researchers and practitioners are invited to submit:

• Full papers (maximum of 8 pages, including references). Original research in NLP for SE, either empirical, theoretical, or showing the practical experience of using NLP techniques and/or NLP tools for addressing software engineering-specific challenges
• Education tools and materials (maximum of 8 pages, including references). Original contributions covering all dimensions of learning and teaching NLP in software engineering. This also includes experience reports providing informal proof by outlining a particular experience connected to education and training, such asa a course, an educational or training method. The submission should translate the experience into practical guidance and insights gained, without the requirement for through evaluation or the application of rigorous research techniques to back its claims.
• Replication Studies and Negative results (maximum of 8 pages, including references). Research papers and reviews focusing on negative results or the reproducibility of previously published work. We believe that publishing negative results, alongside positive ones, provides a more holistic view of the research landscape, fostering transparency, credibility, and the elimination of publication bias.
• Short and demonstration papers (maximum of 4 pages, including references). Work that describes novel techniques, tools, ideas, and positions that have yet to be fully developed; or are a discussion of the importance of a recently published NLP result by another author in setting a direction for the SE community, and/or the potential applicability (or not) of the result in an industrial context.
• Position papers (maximum of 2 pages, including references). Contributions that analyze trends in NLBSE and raise issues of importance. Position papers are intended to seed discussion and debate at the workshop, and thus will be reviewed with respect to relevance and their ability to spark discussions.
• Tool Competition entries (maximum of 4 pages, including references). We invite researchers, students, and tool developers to design innovative solutions to tackle the automated classification of code comments and automated skill classification. For submissions of this kind, please refer to the instructions detailed in the tool competition webpage.

In all cases, papers should address a problem in the software engineering domain or combine elements of NLP research with other concerns in the software engineering lifecycle. Examples of problems in the software engineering domain include (but are not limited to): key information identification and extraction from natural language software artifacts; elicitation, modeling, and verification of requirements; generation of source code documentation; software verification and validation support; classification, summarization, and prioritization of development tasks; changes, developers, and solutions recommendation; maintenance effort minimization; quality assessment of natural language software artifacts.

The solution should apply NLP-based approaches and/or models such as (but not limited to) textual analysis, text summarization, topics or aspects modeling and extraction, machine translation, natural language parsing, semantic parsing, natural language generation, sentiment analysis, discourse analysis.